mlir::Builder builder(ctx);

  // Collect devices with attached metadata.
  llvm::SmallVector<mlir::NamedAttribute, 8> devices;
  devices.reserve(device_set->devices().size());

  // For device that do not have any metadata, or if we failed to parse metadata
  // from the DeviceSet, we add a unit attribute to the `tf.devices` attribute.
  for (Device* device : device_set->devices()) {

  llvm::SmallVector<std::unique_ptr<Device>, 2> devices;
  devices.push_back(FakeDevice::Make(cpu0));
  devices.push_back(FakeDevice::Make(gpu0, "compute capability: 7.0"));
  devices.push_back(FakeDevice::Make(gpu1));

  DeviceSet device_set;
  for (auto& device : devices) device_set.AddDevice(device.get());
  AddDevicesToOp(*module_ref, &device_set);

  auto devices_attr =

        "Multiple CPU devices ", device_info_cache.DebugString(devices)));
  }

  if (multiple_gpu_devices) {
    FAILED_TO_PICK_DEVICE(errors::Internal(
        "Multiple GPU devices ", device_info_cache.DebugString(devices)));
  }

  if (multiple_unknown_devices) {
    FAILED_TO_PICK_DEVICE(errors::Internal(
        "Multiple unknown devices ", device_info_cache.DebugString(devices)));
  }

		pods             []*v1.Pod
		devices          []*podresourcesv1.ContainerDevices
		expectedResponse *v1alpha1.ListPodResourcesResponse
	}{
		{
			desc:             "no pods",
			pods:             []*v1.Pod{},
			devices:          []*podresourcesv1.ContainerDevices{},
			expectedResponse: &v1alpha1.ListPodResourcesResponse{},
		},
		{
			desc: "pod without devices",
			pods: []*v1.Pod{
				{

func New(devEntries []PodDevicesEntry, devices map[string][]string) DeviceManagerCheckpoint {
	return newV2(devEntries, devices)
}

func newV2(devEntries []PodDevicesEntry, devices map[string][]string) DeviceManagerCheckpoint {
	return &Data{
		Data: checkpointData{
			PodDeviceEntries:  devEntries,
			RegisteredDevices: devices,
		},
	}
}

// MarshalCheckpoint returns marshalled data

	// owning device plugin to allow setup procedures to take place, and for
	// the device plugin to provide runtime settings to use the device
	// (environment variables, mount points and device files).
	Allocate(pod *v1.Pod, container *v1.Container) error

	// UpdatePluginResources updates node resources based on devices already
	// allocated to pods. The node object is provided for the device manager to

    auto device = std::make_unique<XlaDevice>(session_options, options);

    Status status = device->UseAcceleratorDeviceInfo();
    if (!status.ok()) {
      LOG(INFO) << "Ignoring visible " << DEVICE_GPU_XLA_JIT
                << " device. Device number is " << i << ", reason: " << status;
      continue;
    }

    devices->push_back(std::move(device));
  }
  return absl::OkStatus();
}

 public:
  Status ListPhysicalDevices(std::vector<string>* devices) override;
  Status CreateDevices(const SessionOptions& options, const string& name_prefix,
                       std::vector<std::unique_ptr<Device>>* devices) override;
};

Status XlaCpuDeviceFactory::ListPhysicalDevices(std::vector<string>* devices) {
  XlaDeviceFlags* flags = GetXlaDeviceFlags();

// CHECK: device = "/TPU:2"
// CHECK: "tf.opA"


// Tests devices are remapped if device is in replicate devices.
// CHECK-LABEL: func @remap_device
func.func @remap_device() {
  tf_executor.graph {
    %0 = tf_executor.island {
      tf_device.replicate {n = 2 : i32, devices = {CORE_0 = ["/CPU:0", "/GPU:1"]}} {
        "tf_device.launch"() ({
          "tf.opA"() : () -> ()
          tf_device.return

};

// Caches some miscellaneous information about TF devices.  Thread compatible.
class DeviceInfoCache {
 public:
  bool IsGpu(DeviceId device) const { return is_gpu_[device.id()]; }
  bool IsCpu(DeviceId device) const { return is_cpu_[device.id()]; }

  absl::string_view GetNameFor(DeviceId device) const {
    return names_[device.id()];
  }

  absl::StatusOr<DeviceId> GetIdFor(absl::string_view name);